Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation
- URL: http://arxiv.org/abs/2205.12445v1
- Date: Wed, 25 May 2022 02:26:34 GMT
- Title: Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation
- Authors: Akash Doshi, Manan Gupta and Jeffrey G. Andrews
- Abstract summary: We develop an unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model.
We then formulate channel estimation from a limited number of pilot measurements as an inverse problem.
Our proposed framework has the potential to be trained online using real noisy pilot measurements.
- Score: 35.62977046569772
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Future wireless systems are trending towards higher carrier frequencies that
offer larger communication bandwidth but necessitate the use of large antenna
arrays. Existing signal processing techniques for channel estimation do not
scale well to this "high-dimensional" regime in terms of performance and pilot
overhead. Meanwhile, training deep learning based approaches for channel
estimation requires large labeled datasets mapping pilot measurements to clean
channel realizations, which can only be generated offline using simulated
channels. In this paper, we develop a novel unsupervised over-the-air (OTA)
algorithm that utilizes noisy received pilot measurements to train a deep
generative model to output beamspace MIMO channel realizations. Our approach
leverages Generative Adversarial Networks (GAN), while using a conditional
input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS)
channel realizations. We also present a federated implementation of the OTA
algorithm that distributes the GAN training over multiple users and greatly
reduces the user side computation. We then formulate channel estimation from a
limited number of pilot measurements as an inverse problem and reconstruct the
channel by optimizing the input vector of the trained generative model. Our
proposed approach significantly outperforms Orthogonal Matching Pursuit on both
LOS and NLOS channel models, and EM-GM-AMP -- an Approximate Message Passing
algorithm -- on LOS channel models, while achieving comparable performance on
NLOS channel models in terms of the normalized channel reconstruction error.
More importantly, our proposed framework has the potential to be trained online
using real noisy pilot measurements, is not restricted to a specific channel
model and can even be utilized for a federated OTA design of a dataset
generator from noisy data.
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